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	Analisi eQTL assistita da Machine Learning	Analisi di Arricchimento dei Percorsi
Campo	Bioinformatica	Bioinformatica
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	2015 (key ML-eQTL methods; foundational eQTL work: Jansen & Nap 2001)	2003–2005
Ideatore	Gamazon et al. (PrediXcan), Zhou & Troyanskaya (DeepSEA); broader field ca. 2015-onward	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Tipo	Statistical-computational genomics pipeline	Statistical functional annotation method
Fonte seminale	Gamazon, E. R., Wheeler, H. E., Shah, K. P., Mozaffari, S. V., Aquino-Michaels, K., Carroll, R. J., ... & Im, H. K. (2015). A gene-based association method for mapping traits using reference transcriptome data. Nature Genetics, 47(9), 1091-1098.	Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.
Alias	ML-assisted eQTL analysis, ML eQTL mapping, deep learning eQTL, predictive eQTL modeling	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Correlati	6	6
Sintesi	Machine learning-assisted eQTL analysis integrates supervised learning models — ranging from elastic-net regression to deep neural networks — into the classical eQTL framework to predict and map genetic variants that regulate gene expression. By training predictive models on reference panels (e.g., GTEx), the approach enables imputation of gene expression in cohorts lacking RNA data, substantially increasing statistical power and enabling trans-tissue generalisation.	Pathway enrichment analysis (PEA) is a statistical approach that takes a list of genes or proteins of interest — typically derived from a differential expression or proteomics experiment — and identifies which pre-defined biological pathways or functional gene sets are represented more often than expected by chance. By mapping individual molecular changes onto curated pathway knowledge bases such as KEGG, Gene Ontology, or Reactome, PEA translates long gene lists into interpretable biological processes, making it a central tool in the post-analysis of high-throughput omics experiments.